CHARACTERIZING THE CO-OCCURRENCE OF SUBSTANCE USE AND MENTAL HEALTH SYMPTOMS AMONG ADOLESCENTS IN GENERAL POPULATION AND CLINICAL SAMPLES
Bibliographic record
Abstract
Background: Despite policy and practice guidelines highlighting the need to identify and treat substance use early and concurrently with other mental health symptoms, efforts remain uncoordinated and guidelines lack specificity. Limited evidence characterizing patterns and correlates of co-occurring substance use and mental health symptoms hinders our ability to effectively address these concerns early during adolescence. This dissertation deepens our understanding of the patterns and correlates of co-occurring substance use and mental health symptoms among adolescents, how to collect relevant data in inpatient settings, and how to rigorously analyze and report findings. Methods: The first paper is a systematic review of 70 cluster-based studies examining patterns of multiple substance use among adolescents. The second examines patterns and correlates of co-occurring substance use and mental health symptoms through multilevel latent profile analysis and multilevel multinomial regression using a large, representative sample of secondary students and schools across Ontario. The third paper is a pilot study examining the feasibility, acceptability, and importance of standardized assessments of substance use and mental health symptoms in an adolescent psychiatric inpatient unit. Results: The substantive findings of this work include: 1) multiple substance use is common; 2) co-occurrence of substance use and mental health symptoms is common, though not universal; 3) substance use may be related to mental health symptom severity, comorbidity, and hospital service use; 4) school climate, belonging, and safety represent important targets for school-based interventions; and 5) adolescent psychiatric inpatient units may represent important contexts for standardized assessments, though more professional training and standardization in assessments and interventions are needed. Methodological recommendations are also presented to improve the collection, analysis, and reporting of similar work in the field. Conclusions: Collectively, this dissertation provides novel, timely, and actionable insight into adolescent substance use patterns, correlates, and potential targets for assessment and intervention efforts.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".